Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

Sensors

Lei Huang¹ 

[1] Automation Department, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, China; E-Mail

关键词: random noise modeling;    robust Kalman filtering;    ARMA modeling;   

DOI  :  10.3390/s151025277

来源: mdpi

【 摘 要 】

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.